ICES CM 2012/A:06

Ecological resilience research in practice: the experience of the Barents Sea Ecosystem Resilience project (BarEcoRe)

Benjamin Planque, Grégoire Certain, Kathrine Michalsen, Magnus Wiedmann, Susanne Kortsch, Lis Lindal Jørgensen, Raul Primicerio, Michaela Aschan, Padmini Dalpadado, Mette Skern-Mauritzen, Edda Johannesen

The BarEcoRe project investigates the resilience of the Barents Sea Ecosystem under global environmental changes. Studying resilience of marine ecosystems is fundamental in the current context of climate change and intense fishing pressure, but the theoretical framework and the practical tools to define, quantify and monitor resilience are still being developed. So far, the BarEcoRe project has studied several aspects of the Barents Sea ecosystem structure and function with a view to contribute to a better understanding, monitoring and projection of the resilience of this system. In this presentation, we report on the aspects of resilience that are specifically addressed during the BarEcoRe project. We present how some of the results of the BarEcoRe project can contribute to resilience research through either dynamical or structural approaches. This collection of research topics reveals the diversity of aspects that need to be considered for resilience studies conducted at the ecosystem level.

Keywords: ecosystem resilience, taxonomic diversity, functional diversity, food-web topology, community structure, macro-ecological models, spatial distribution models

Contact author: Benjamin Planque, Institute of Marine Research, POSTBOKS 6404, 9294 Tromsø, Norway. E- mail: [email protected], Phone: +47 77609721, Fax: +47 77609001

1 Aim and scope of BarEcoRe

What controls the resilience of the Barents Sea ecosystem? How does it vary with time and space? Can we anticipate possible future changes in the resilience of the Barents Sea? These were the general questions which motivated the BarEcoRe project which began in June 2010 and will terminate in May 2013. BarEcoRe, which stands for Barents Sea Ecosystem resilience under global environmental change, is a research project in applied ecology with the initial goal to evaluate the effects of global environmental change on the future structure and resilience of the Barents Sea ecosystem. BarEcoRe was initially structured around five questions: • What are the key characteristics of past temporal and spatial variations in fish and benthos communities and how are these related to past climate variability and fishing pressure? • How does climate variability and change propagate through the Barents Sea ecosystem and influences species interactions? • How can the combined effects of fisheries and climate modify the spatial distribution of plankton, benthos and fish species in the Barents Sea? • What determines vulnerability or resilience of the Barents Sea ecosystem and how will these be affected by possible future changes in climate and fisheries regimes? • Can we detect early warning signals and can we evaluate management strategies with regards to ecosystem resilience? Addressing these questions requires an appropriate combination of theoretical framework, numerical tools and field data. On the basis of the experience acquired during the BarEcoRe project, we report below what we have identified as the key fundamentals to study ecosystem resilience in practice.

Ecosystem monitoring

Monitoring multiple components of the ecosystem is paramount to investigations of ecosystem resilience. The Barents Sea benefits from a long history of monitoring, from physics to whales. During the last two decades the development of the ‘ecosystem approach to fisheries management’ led to an evolution in the recommendation for collection of oceanic data (FAO, 2003). In the Barents Sea, this resulted in the establishment of a dedicated ecosystem surveys in the early 2000’s (Olsen et al., 2011). The Barents Sea ecosystem survey turned out to be the essential primary data source for this project. The key elements of the data collection include: • Simultaneous collection of data across many trophic levels, including phytoplankton, zooplankton, benthos, fish, mammals and birds, • Collection of physical and chemical data, • taxonomic identification at a high resolution and with quality check, • large spatial coverage (>1 million square nautical miles), • Annual repetition over multi-decadal periods

Practical approaches to resilience

The literature on resilience is rich, spans a wide range of disciplines sometimes far away from ecology, and may not always be consistent with the way resilience is understood, defined and eventually quantified (Strunz, 2012). In ecological studies, resilience is usually broadly defined as ‘the ability of a system to absorb disturbances and still maintain structure and functions’. But such definition is too vague to be practical for applied quantitative ecological research in the Barents Sea.

2 In the BarEcoRe project, we attempted to classify concepts and metrics related to resilience according to their applicability in quantitative ecology (Planque et al., poster A:20, this conference). We identify a first class of resilience-related concepts that are often conceptually vague but useful to promote creative thinking, transdisciplinary exchanges, and participative processes for complex problem solving. These include for example terms such as identity, adaptability and transformability. On the other side, precision is necessary to ensure scientific rather than faith based thinking, to set the limits of validity of particular concepts and to ensure testability of concepts against empirical evidence. These criteria are used to define a second class of concepts of direct relevance to quantitative ecology. These can be further divided into structural and dynamic approaches to resilience. The structural approach to resilience relies on a couple of paradigms borrowed from the literature on diversity-stability relationships, functional diversity and trophic network structure. It is currently the only available framework allowing the use of the resilience concept in large ecosystems composed of hundreds or thousands of species. Structural studies of resilience are typically concerned with the measurement of diversity, redundancy and modularity of ecosystem components (Levin and Lubchenco, 2008). The dynamic approach to resilience comprises temporal analysis of systems close to equilibrium (as is the case for return rate and elasticity) or in transition between multiple stable states (e.g. tipping points, hysteresis and regime shifts). In practice dynamic studies are most conducted on small, closed systems which dynamics is well known and for which equilibrium points can be defined, usually on the basis of simple mathematical models (Scheffer et al., 2001).

Examples of results

Spatial patterns of biodiversity How to choose a proper set of metrics to describe the structural properties of the Barents Sea biodiversity, how to compute and aggregate them at a spatial scale consistent with management needs, and how to interpret them in the context of resilience? To address these questions, we analysed the spatial patterns of biodiversity across trophic levels and within and across benthic, demersal and pelagic communities, based on the Barents Sea ecosystem survey data from 2004 – 2008. The Barents Sea was geographically divided in several polygons, taken as homogeneous ecosystem sub-units. For each sub-unit, environmental and biological data were aggregated, and structural properties of resilience, i.e. biodiversity metrics, were computed. In addition, environmental descriptors including physico-chemical descriptors, productivity, zooplankton biomass, and fishing pressure where aggregated in the same geographical sub-units in order to provide the context within which biodiversity metrics can be interpreted. The use of several taxonomic, phylogenetic and functional biodiversity metrics was considered for 4 major communities: benthic, demersal, pelagic, and top predators. For each metric/community combination, total (γ) diversity within a polygon can be partitioned between α- (intra-site) and β- (inter-site) component, which can be interpreted as local and spatial measures of diversity, respectively (Figure 1). Additionally, topological properties of the food web were investigated, offering measures of the food web organisation within each polygons at the ecosystem scale.

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Figure 1. Spatial distribution of Hill’s alpha and beta taxonomic diversity in the sub-regions of the Barents Sea for the demersal fish community (2004-2009).

The preliminary, methodological results indicate that: i) Although there is a large range of diversity metrics available on taxonomic, phylogenetic and functional diversity, many where highly correlated. Multivariate statistics suggested that most information was retained by using a couple of metrics, one for the α-diversity and one for the β-diversity; ii) presence – absence data clearly lacked sensitivity in comparison to abundance data; iii) numbers of individuals and biomass data gave different biodiversity patterns. From a functional point of view, it can be argued that biomass is more important than numbers, and biomass is also more frequently measured than numbers during the survey. Hence, final analyses will preferably be based on biomass-data. iv) The use of polygons, identified to enclose relatively homogenous areas, provided a useful framework for aggregating the data across communities. Multivariate analyses demonstrated that the polygons captured the regional variation in environmental drivers well.

Functional diversity Simple measures of biodiversity, such as species richness, are relatively easy to obtain but such measures do not account for functional differences between species. In recent years, several measures of functional diversity (FD) were proposed (e.g. Petchey and Gaston, 2002) in order to explicitly account for biological attributes, recognizing the fact that some species are functionally more similar than others. Still, only a few number of studies have so far focused on functional diversity in marine communities (e.g. Halpern and Floeter, 2008).

We have calculated functional diversity of the Barents Sea fish community for the years 2004-2009, based on the combination of fish distributional data from the Norwegian-Russian ecosystem surveys and on an extensive matrix of species’ functional traits (Wiedmann et al., presentation A:13, this conference). Our analyses indicate that FD largely follows the spatio-temporal patterns of species richness, while 14% of the variation in FD remained unexplained by species richness. A persistent field of high FD was observed in central-western areas, having a varying extension to the North and South between years. On the contrary, we identified a persistent field of low FD in the south-eastern corner of the Barents Sea (Figure 2). In years when the central FD field clearly extended towards the North (like in 2008), a corresponding extension of the south-eastern field of low FD was observed. These findings may reflect the spatial dimension of the Barents Sea fish community adaptability.

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Figure 2. Mean (left) and standard deviation (right) in functional diversity of fish in the Barents Sea.

Vulnerability of benthic communities

How benthic species may be vulnerable to trawling disturbance depends on a number of ecological traits, which include life-expectancy, mobility, size, morphology or habitat preference. Using data on benthic species distribution and ecological traits, we developed an index of the vulnerability of the benthic community to trawling disturbance, and applied it to the whole Barents Sea (Jørgensen et al., poster A:25, this conference). From the observed relationship between functional diversity and vulnerability, we proposed a classification of benthic communities in three categories: “vulnerable”, “impacted”, and “degraded”. This classification is evaluated through the relationship between vulnerability and bottom trawling intensity, estimated from the Norwegian vessel monitoring system data.

Environment-based models of biodiversity How will future environmental conditions affect resilience? This central question to the BarEcoRe project was approached using “Macro-Ecological Models” (MEMs) models in which environmental conditions are described by ocean climate and resilience is approached by a structural property of the system: biodiversity. We assessed the predictive performance of MEMs, by evaluating the influence of methodological choices on the their predictive power (Certain et al, presentation A:09, this conference). Three methodological choices were assessed: (1) the choice of the currency used for measuring abundance of each species, i.e. numbers or biomasses; (2) the choice of community assembly, i.e. the rules according to which species were selected to form the community under focus, e.g. taxonomic, trophic or demographic communities; and (3) the choice of the biodiversity metric. We show that in the Barents Sea, biodiversity can be better predicted when based on biomass rather than number. Furthermore, prediction performance is higher when communities are composed of species with a narrow set of ecological traits (Figure 3). Finally, higher predictive performance is obtained with diversity indices that give heavy weight to rare species, but still take into account species relative frequencies. We recommend that models constructed to project Barents Sea biodiversity in the future should take these effects into account.

5 Fish total Low−fecundity Fish High−fecundity Fish

8 8 8

6 6 6

4 4 4

2 2 2

Figure 3. Environment based predictions of the spatial distribution of fish taxonomic diversity in the Barents Sea, using Hill’s diversity index. Predictions for all demersal fishes (left), for fish communities with low fecundity (left) and high fecundity (right).

Foodweb structure The structure of the food web can be used as a structural descriptor of ecosystem resilience. Food web topology, the ensemble of species and of their trophic interactions, can be depicted through a range of metrics. These include species richness, connectedness, compartmentalization and degree of omnivory. A better ecological understanding of ecosystem resilience can be achieved via the description these topological properties, and by understanding how the specific metrics relate to the dynamics of the system. We conducted an analysis of the food web topologies for the Atlantic and the Arctic regions of the Barents Sea (Kortsch et al., poster A:23, this conference). The Atlantic food web consisted of 122 species and 958 links, whereas the Arctic food web consisted of 79 species and 491 links (Figure 4). Yet, degree of compartmentalisation and omnivory did not differ much between the two systems. The food web metric that showed greatest difference between the areas was connectance, i.e. the proportion of possible links in a food web that actually occur.

Figure 4. Food web networks portraying degree of compartmentalization: A) Atlantic Barents Sea (comp=0.24). B) Arctic Barents Sea (comp=0.28). Different compartments are represented with difference colours. The Atlantic region has 5 compartments, whereas the Arctic has 4.

6 Past variations in the Barents Sea ecosystem structure and forcing Long time-series of data from the Barents Sea (BS) were analysed to contrast the climate, fishing pressure, plankton, pelagic fish, demersal fish, and interactions between trophic levels in a recent decade (2000–2009) with the period 1970–1999 (Johannesen et al., 2012). During the past four decades, fishing pressure and climatic conditions have varied greatly in the Barents Sea, and stock levels have fluctuated substantially. Trophic control has changed from mainly bottom–up to top–down, then back to mainly bottom–up. No clear evidence for persistent ecological regimes was found, although the dynamics of the Barents Sea ecosystem changed substantially when the herring returned and the capelin collapsed for the first time in the early 1980’s. After this, a period with strong fluctuations in capelin coincided with increased variability in capelin prey and predators abundance and fitness. The past decade has been the warmest on record, with a decrease in ice coverage and increase of mixed water (between 0-3 degrees) at the expense of arctic sub-zero water. There have been large stocks of both demersal and pelagic fish, and increasing abundances of krill and shrimp. Except perhaps for the rather less-studied Arctic species, the short-term effect of the recent warming has been positive for BS stocks. However, many of the long-established between temperature and biological parameters, as well as interactions between prey and predators appear to be changing (Figure 5), making it difficult to extrapolate the future effects of warming on the BS ecosystem.

Figure 5. Correlations between predators and prey, calculated on 15-year sliding windows (centred). Positive correlations indicate bottom-up control whilst negative correlations indicate top-down control. Reproduced from Johannesen et al. (2012).

Stochastic Dynamic Modelling The dynamic approach to resilience is usually applicable on small, close systems for which dynamics is well known and for which equilibrium points can be mathematically defined. In practice, large ecosystems such as the Barents Sea do not fall in this category, and there exist no established theory that can be used to define the reference state of an ecosystem. The stochastic dynamic ecosystem modelling approach was developed to provide a reference model for ecosystem dynamics based on a minimal set of assumption. The model concepts are borrowed from Mullon et al. (2009). The main ideas are: 1) because of their structural super-complexity, non-linearities and capacity to constantly re-organise themselves, ecosystems are by nature unpredictable and 2) ecosystems are not totally unpredictable either because they are constrained by fundamental physical laws (such as conservation of mass and energy) as well as physiological and behavioural limits of their constitutive species. Given these few constraints it is possible to simulate realistic ecosystem dynamics (Lindstrøm

7 et al., poster A: 19, this conference) that can serve as a reference against which observed ecosystem dynamics can be compared (Figure 6). We used this modelling framework to investigate whether empirical tools for the detection of regime shifts are capable of discriminating between cases when an ecosystem has shifted from a state to another in response to external causes from cases where such large amplitude changes are part of the internal dynamics of the ecosystem (Planque et al., presentation A:05, this conference).

Copepods Euphausiids Benthos 25 7 25

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biomass (tons.km biomass (tons.km biomass (tons.km 2 5 5 1

0 0 0 300 320 340 360 380 400 300 320 340 360 380 400 300 320 340 360 380 400 year year year

Pelagics Demersals Mammals

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3 1 0.2 2 biomass (tons.km biomass (tons.km biomass (tons.km 0.5 0.1 1

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Figure 6. An illustration of the temporal dynamics of biomass of copepods, euphausiids, benthos, pelagic fishes, demersal fishes and marine mammals for 100y, as simulated by the Stochastic Dynamic Foodweb model.

Conclusions

The aim of the BarEcoRe project, to understand, measure and project the resilience of the Barents Sea ecosystem, turned out to be a rather complicated and polyform task. From the multiplicity of approaches carried out so far within this project we can draw the following preliminary conclusions: • There is no unique way to define and measure ecosystem resilience. Studies on ecosystem resilience must recognise this diversity and make explicit which precise concept(s), method(s) and metrics are used. • Ecosystem monitoring is paramount to investigations of ecosystem resilience, especially in large, open systems as the Barents Sea. This implies basinwide and multidecadal observations of many species and environmental factors, with a high degree of spatial and taxonomical resolution. • In quantitative ecology, two classes of approach exist to study ecosystem resilience: structural and dynamical approaches. With the data available in the Barents Sea (and in most marine systems) the structural approach is by far the most accessible. • Different structural approaches yield different results. Evaluation of ecosystem resilience should not be based on a single structural metric. • Projecting future biological diversity patterns is more likely to be successful for biomass distribution and for well defined ecological communities. • Dynamical studies of ecosystem resilience often rely on ecosystem reference states and are unlikely to be conducted without numerical models. Whether current ecosystems models are appropriate to do so remains to be evaluated. • Structural indices of resilience can readily be used to inform regional management institutions. For examples, Productive areas with high taxonomic and functional diversity

8 (e.g. central Barents Sea – Polar front) are more likely to sustain harvesting than areas with low functional and taxonomic diversity (e.g. south-eastern Barents Sea). Norway has begun to implement Ecosystem Based Management (EBM) by using management plans for the Norwegian sector of the Barents Sea, including the fishery protection zone around Svalbard (Anonymous, 2006), the Norwegian Sea (Anonymous, 2009), and North Sea. Following international guidelines for EBM, the plans provide an overall framework for managing all human activities in the areas to ensure the continued health, production, and functions of the ecosystems (Olsen et al., 2007). Currently, the Barents Sea management plan relies mostly on single species indicators. The integrated ecosystem measures provided by the project BarEcoRe will open for a more genuine approach to ecosystem assessment in future management plans (Primicerio et al., poster A:26, this conference)

References

Anonymous. 2006. Integrated management of the marine environment of the Barents Sea and the sea areas off the Lofoten Islands. Norwegian Ministry of the Environment. 144 pp. Anonymous. 2009. Integrated management of the marine environment of the Norwegian Sea. Norwegian Ministry of the Environment. 148 pp. Certain, G., Dormann, C. F., and Planque, B. 2012. Macro-Ecological Models of biodiversity metrics: How much can be explained by environmental change? ICES CM, 2012/A:09. FAO. 2003. Fisheries management 2. The ecosystem approach to fisheries. 112pp pp. Halpern, B. S., and Floeter, S. R. 2008. Functional diversity responses to changing species richness in reef fish communities. Marine Ecology Progress Series, 364: 147-156. Johannesen, E., Ingvaldsen, R. B., Bogstad, B., Dalpadado, P., Eriksen, E., Gjøsæter, H., Knutsen, T., et al. 2012. Changes in Barents Sea ecosystem state, 1970-2009: climate fluctuations, human impact, and trophic interactions. ICES Journal of Marine Science, 69: 880-889.

Jørgensen, L. L., Certain, G., Johannesen, E., Lubin, P., Planque, B., Primicerio, R., Thangstad, T., et al. 2012. Identifying thresholds of resilience within the benthos of the Barents Sea ecosystem? ICES CM, 2012/A:25. Kortsch, S., Aschan, M., Planque, B., Wiedmann, M. A., Jørgensen, L. L., Dalpadado, P., Skern- Mauritzen, M., et al. 2012. Food web topologies of the Barents Sea. ICES CM, 2012/A:23.

Levin, S. A., and Lubchenco, J. 2008. Resilience, Robustness, and Marine Ecosystem-based Management. BioScience, 58: 27-32. Lindstrøm, U., Planque, B., and Subbey, S. 2012. A dynamic stochastic food web model for the Barents Sea ecosystem. ICES CM, 2012/A:19. Mullon, C., Fréon, P., Cury, P., Shannon, L., and Roy, C. 2009. A minimal model of the variability of marine ecosystems. Fish and Fisheries, 10: 115-131. Olsen, E., Gjosaeter, H., Rottingen, I., Dommasnes, A., Fossum, P., and Sandberg, P. 2007. The Norwegian ecosystem-based management plan for the Barents Sea. ICES Journal of Marine Science, 64: 599-602. Olsen, E., Michalsen, K., Ushakov, N. G., and Zabavnikov, V. B. 2011. Chapter 10.6. The ecosystem survey. In The Barents Sea - ecosystem, resources and management. Half a century of Russian-Norwegian cooperation., pp. 604-608. Ed. by T. JAKOBSEN, and V. K. OZHIGIN. Petchey, O. L., and Gaston, K. J. 2002. Functional diversity (FD), species richness and community composition. Ecology Letters, 5: 402-411.

9 Planque, B., Certain, G., Primicerio, R., Michalsen, K., Jørgensen, L. L., Aschan, M., Dalpadado, P., et al. 2012. Ecological resilience for ecologists. ICES CM, 2012/A:20. Planque, B., and Lindstrøm, U. 2012. Defining reference states for ecosystems, an approach through dynamic stochastic modeling. ICES CM, 2012/A:05. Primicerio, R., Aschan, M., Wiedmann, M., Kortsch, S., Planque, B., Certain, G., Johannesen, E., et al. 2012. Operationalizing ecological robustness and resilience for ecosystem based management. ICES CM, 2012/A:26. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C., and Walker, B. 2001. Catastrophic shifts in ecosystems. Nature, 413: 591-596. Strunz, S. 2012. Is conceptual vagueness an asset? Arguments from philosophy of science applied to the concept of resilience. Ecological Economics, 76: 112-118. Wiedmann, M. A., Aschan, M., Certain, G., Dolgov, A., Greenacre, M., Johannesen, E., Planque, B., et al. 2012. Functional diversity of the Barents Sea fish community: structure and drivers. ICES CM, 2012/A:13.

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